Artificial Intelligence is defined as a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation. AI is a topic in nearly every conference and at many dinner tables. It assists us in every area of our lives, whether we’re trying to read our emails, get driving directions, or start a new business.
There is significant misunderstanding accompanying use of Artificial Intelligence. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us, enthralling us and frightening us in equal measure. Yet, despite this prominence, AI is still a surprisingly fuzzy concept and a lot of questions surrounding it are still open. I find really strange how, even though AI is fuzzy and confusing, every startup wants to be working with AI, is working with AI or says they are working with AI. Plot twist: sometimes AI doesn’t solve any problems. Plot twist #2: AI may make problems even worse. Just look at what happened with Barnes and Noble earlier this month and you'll get what I am talking about.
Of course, AI may be useful but I don’t get why so many startups invest thousands of dollars to have their own AI thing without even thinking we have a major challenge on our hands in bringing together computers and humans in ways that enhance human life and actually solves problems and not just steals data.
Most of what is labeled AI today is actually machine learning, a term in use for the past several decades. Machine learning is an algorithmic field that blends ideas from statistics, computer science and many other disciplines to design algorithms that process data, make predictions, and help make decisions.
In the early 2000s companies such as Amazon were already using machine learning throughout their business, solving mission-critical, back-end problems and building innovative consumer services such as recommendation systems.
As datasets and computing resources grew rapidly, it became clear that machine learning would soon power any company in which decisions could be tied to large-scale data. That’s when the term ‘data science’ emerged reflecting the need of algorithms experts to partner with database and distributed-systems experts to build scalable machine learning systems.
This has recently been rebranded as "Artificial Intelligence" and the lack of knowledge of how this technologies work and when they are really useful most of the time makes machine learning systems look like this comic:
Of course I don’t pretend every startup to ditch AI but it would be nice to stop and think if it actually will make a difference, if it will have the desired results or if you are just building your AI because it’s cool and trendy. I believe we should embrace AI as a human-centric engineering discipline and its main objective should be solving real problems and optimizing processes but, bear in mind sometimes real people can do a better and cheaper job.